Causality-aware Enhanced Model for Multi-hop Question Answering over Knowledge Graphs

To improve the performance of knowledge graph-based question answering system (KGQA), several approaches have been developed to construct a semantic parser based on entity linking, relation identification and logical/numerical structure identification. However, existing methods arrive at answers only by maximizing the data likelihood only on the sparse or imbalanced explicit relations, ignoring the potentially large number of latent relations. It makes KGQA suffer from a high level of spurious entity relations and missing link challenge. In this paper, we propose a causal filter (CF) model for KGQA (CF-KGQA), which performs causal interference on the relation representation space to reduce the spurious relation representation in a data-driven manner, i.e., the goal of this work is to comprehensively discover disentangled latent factors to alleviate the spurious correlation problem in KGQA. The model comprises a causal pairwise aggregator (AP) and a disentangled latent factor aggregator (AC). The former filters out most spurious entity relations inconsistent to their dense groups’ neighborhood, and generates a causal pairwise matrix among all the candidate relations. The latter learns the latent relation representation via an encoder–decoder on the causal pairwise matrix. It disconnects the latent factor and the causal confounder beneath the knowledge embedding space by causal intervention. To prove the effectiveness and efficiency of the proposed approach, we test CF-KGQA and other state-of-the-art methods on four public real-world datasets. The experiments indicate that our approach outperforms the recent methods and is also less sensitive to the spurious correlation problem, thus demonstrating the robustness of CF-KGQA.